pacman::p_load(ggiraph, plotly,
patchwork, DT, tidyverse) Hands-on Exercise 3
Part 1: Programming Interactive Data Visualisation with R
1.1 Getting Started
To begin this exercise, load the necessary R packages that support interactive and linked visualisations.
1.2 Importing Data
We’ll work with a dataset containing students’ exam scores. This data will be used throughout the exercise to create various visualisations.
exam_data <- read_csv("Data/Exam_data.csv")Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
1.3 Interactive Visualisation with ggiraph
1.3.1 Dot Plot with Tooltips
This plot introduces basic interactivity where hovering over dots reveals student IDs.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)1.3.2 Displaying Multiple Tooltip Fields
Add richer information to tooltips by concatenating ID and class.
exam_data$tooltip <- c(paste0(
"Name = ", exam_data$ID,
"\n Class = ", exam_data$CLASS))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = exam_data$tooltip),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 8,
height_svg = 8*0.618
)1.3.3 Customising Tooltip style
Modify the tooltip appearance using inline CSS.
tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list( #<<
opts_tooltip( #<<
css = tooltip_css)) #<<
) 1.3.4 Displaying Summary Statistics in Tooltip
Show aggregate values like mean and SEM with hover feedback.
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, size = 0.2
)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)- replace the size argument with linewidth in
geom = "errorbar"layer to resolve the warning.
Here is the corrected version of the code:
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, linewidth = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)1.3.5 Hover Effect with data_id
Implement visual effects such as highlighting and opacity change on hover.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
) 1.3.6 Styling the Hover Effect
Implement visual effects such as highlighting and opacity change on hover.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 1.3.7 Tooltip + Hover Integration
Implement visual effects such as highlighting and opacity change on hover.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 1.3.8 Adding Click Actions with onclick
Clicking a dot opens a URL with more student information.
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618) 1.3.9 Coordinated Views
Synchronise hover effects across multiple views using shared IDs.
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 + p2),
width_svg = 6,
height_svg = 3,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 1.4 Interactive Visualisation with Plotly
1.4.1 Scatter Plot
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
1.4.2 Encoding by Race
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
1.4.3 ggplot Conversion
Convert existing ggplot2 objects into interactive plots.
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)1.4.4 Coordinated Linked Views
Highlight observations across multiple charts using highlight_key() and subplot().
d <- highlight_key(exam_data)
p1 <- ggplot(data=d,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=d,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))1.5 Crosstalk and DT
1.5.1 Interactive Table
Render a searchable, sortable, compact datatable using DT.
DT::datatable(exam_data, class= "compact")1.5.2 Linked Table and Plot
Link DT tables and plots interactively using the crosstalk framework.
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5) Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.
Part 2: Programming Animated Statistical Graphics with R
2.1 Getting Started
Loading the R packages
pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)2.2 Importing the data
Load population data from Excel and prepare it for time-series animation.
col <- c("Country", "Continent")
globalPop <- read_xls("Data/GlobalPopulation.xls",
sheet="Data") %>%
mutate(across(col, as.factor)) %>%
mutate(Year = as.integer(Year))Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(col, as.factor)`.
Caused by warning:
! Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(col)
# Now:
data %>% select(all_of(col))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
2.3 Animated Visualisation with gganimate
2.3.1 Building a static population bubble plot
Create a base plot using geom_point() with aesthetic mappings.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') 
2.3.2 Animated Plot
Use transition_time() and ease_aes() to animate transitions over time.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear') 
2.4 Animated Visualisation with plotly
2.4.1 Using ggplotly()
Convert animated ggplot objects to interactive plotly charts.
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame
ggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
2.4.2 Fix Display Issues
Fix layout issues and hide unnecessary legends to clean up the output.
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7):
Ignoring unknown aesthetics: frame
ggplotly(gg)Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
2.4.3 Directly with plot_ly()
Use plot_ly() directly to generate a bubble chart animated by year.
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
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Summary
In this hands-on exercise, we learned how to create both interactive and animated data visualisations in R. Part 1 focused on interactive graphics using ggiraph, plotly, and DT for better data exploration and presentation. Part 2 introduced animated plots with gganimate and plotly to enhance time-based storytelling. These techniques are especially useful in building dashboards, reports, and applications where user engagement and clarity are key.